The present disclosure generally relates to the field of artificial intelligence in the area of CAD tools. More particularly, the present disclosure relates to the use of artificial intelligence rules in computer aided engineering (CAE) programs and to a similar parts recommendation system using the artificial intelligence rules in the CAE programs.
Many important characteristics of advanced computing applications are changing the way engineers interact with computers and computer programs. New approaches based on artificial intelligence (AI) may be used in various field of engineering. Analysis-based design improvement is an example of an engineering task that may apply the use of AI systems.
Finite element analysis (FEA) is one of the most extensively used numerical methods in the engineering product development process. Knowledge based engineering (KBE) techniques have been applied to FEA to teach, advise, and automate the FEA pre-processing phase mainly involving automatic mesh generation, and verifying calculations. However, the use of AI methods is almost absent in the post-processing phase and the subsequent design modification and improvement of designs. Many early examples present a rule-based approach to automate the optimization of simple components or geometric shapes. KBE applications for analysis-based design improvement are quite scarce, although the need for linking intelligent programs to structural analysis in design is prevalent.
Various software and hardware components are frequently required to perform both geometric modeling and engineering analysis. An independent intelligent advisory system for decision support within the analysis-based design improvement process can be applied more easily. Moreover, using a qualitative description of engineering analysis results, such a system can be more general and cover a wider range of application areas. Intelligent interpretation of analysis results can be used to choose the most suitable design modifications. Thus, what is needed is a system for and a method of extracting meaningful qualitative design information from simulation results and to couple this information to a design modification system as a higher level of representation.